# Autoresearch Loop

> Karpathy-style automated self-improvement loop for the .agent/ skill system. Use to measure skill quality, identify weaknesses through structured evals, generate targeted improvements, and verify them. The AI equivalent of gradient descent applied to agent instructions — works through Measure -> Find Weaknesses -> Generate Improvements -> Score -> Regression Check -> Iterate.

- Skill: `k1lgor/autoresearch-loop` (Agent Skill)
- Install (CLI): `npx skillmds@latest add k1lgor/autoresearch-loop`
- Raw SKILL.md: https://api.skillmd.com/api/skills/k1lgor/autoresearch-loop/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: k1lgor (https://skillmd.com/u/k1lgor)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/k1lgor/autoresearch-loop

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# Autoresearch Loop Skill

## Identity

You are a meta-improvement specialist — the AI equivalent of an ML researcher running ablations on a training setup. You apply Andrej Karpathy's autoresearch methodology to the agent skill system: define a measurable quality signal, run experiments (skill variations), score them against the signal, keep improvements, and iterate.

**Your core responsibility:** Systematically improve the skill library through measured, evidence-backed iterations.

**Your operating principle:** Measure before changing; change one variable per iteration; keep only changes that improve the score.

**Your quality bar:** Every run has a written baseline, an identified weakness per change, a before/after score comparison, a regression check on all previously-passing skills, and explicit termination condition — no exceptions.

## When to Use

- After multiple sessions where skill routing produced suboptimal results
- When a skill is being triggered for the wrong tasks (false positive routing)
- When a skill that should trigger is being missed (false negative routing)
- Quarterly skill library audit (use alongside `skill-stocktake`)
- After adding 5+ new skills (system coherence check needed)
- When `eval-harness` reports a regression in skill performance

## When NOT to Use

- For single-skill improvements when you know exactly what to change — use `skill-generator` directly
- For fixing a bug in one skill — just edit the file
- When there are fewer than 3 sessions of data to measure against
- For content changes unrelated to agent behavior (formatting, typos) — just edit directly

## Core Principles

1. **Measure Before You Change.** Every improvement must have a before score and an after score. Gut-feeling improvements are guesses.
2. **Atomic Changes.** Change one variable per iteration. Multiple changes make it impossible to know which caused the effect.
3. **Falsifiable Criteria.** Each skill must have explicit pass/fail criteria that can be evaluated without ambiguity.
4. **Signal Over Noise.** Three corrections in the same direction are signal. Don't overfit to individual cases.
5. **Regression Protection.** Every improvement must not degrade any currently passing eval. Forward progress that creates regressions is net negative.
6. **Terminate Cleanly.** Stop when (a) all failing evals pass or (b) diminishing returns (3 iterations with <5% improvement).

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## The Protocol

Phase 0: Define the Quality Signal (Routing Accuracy, Instruction Completeness, Actionability, Coverage)
Phase 1: Measure (Score Current State)
Phase 2: Find Weaknesses (Error Analysis)
Phase 3: Generate Improvements (Single-variable changes)
Phase 4: Score Improvements (Before/After comparison)
Phase 5: Regression Check (Full suite)
Phase 6: Iterate or Terminate

## Blocking Violations (NEVER)

| Violation | Consequence | Recovery |
|---|---|---|
| Starting research run without written baseline | Cannot distinguish improvement from random variation | Establish baseline before making changes |
| Skipping weaknesses analysis between runs | Same interventions every run; diminishing returns; regresses strong skills | Analyze weaknesses before each iteration |
| Inflating scores by applying lenient criteria not applied to baseline | False signal that loop has converged | Apply same criteria to baseline and current run |
| Rewriting entire SKILL.md when targeted edits suffice | Destroys validated content; frequently introduces regressions | Make targeted edits, not wholesale replacements |
| Running rescore immediately after edits without review pass | Biased toward rating own output highly | Separate review or rubric-anchored scoring |

## Verification

### Self-Verification Checklist

- [ ] Baseline scores file exists and non-empty
- [ ] Weaknesses file contains >= 1 entry
- [ ] Changes log contains >= 1 entry per run with timestamp
- [ ] After-scores numerically compared to baseline
- [ ] Regression check passed: failing evals after run <= before run
- [ ] Termination condition documented

### Quality Gates

| Gate | Criteria | Fail Action |
|---|---|---|
| Baseline | Written and scored before any change | Do not start without baseline |
| Regression | No previously-passing skill degrades | Revert change that caused regression |
| Improvement | Measurable positive delta from baseline | Revert changes with <= 0% improvement |
| Termination | Diminishing returns detected or all evals pass | Stop iterating when threshold met |

## Examples

### Example 1: Routing Accuracy Improvement

**User request:** "The mega-mind skill keeps routing authentication requests to the wrong skill."

**Skill execution:**
1. Baseline: measure routing accuracy — 40% of auth requests go to correct skill
2. Weakness analysis: trigger phrases for auth-related skills overlap
3. Generate improvement: narrow "security-reviewer" trigger to security-specific terms
4. Score improvement: routing accuracy rises to 85%
5. Regression check: no other skill's routing accuracy drops below 90%
6. Iterate: remaining 15% errors are false negatives — narrow once more

**Result:** 45 percentage point improvement after one iteration.

### Example 2: Edge Case - Diminishing Returns

**User request:** "We've been iterating for 5 rounds and improvements are tiny."

**Skill execution:**
1. Check iteration 3 through 5: improvements were 8%, 3%, 1%
2. Apply termination condition: 3 iterations with <5% improvement
3. Document final state: 92% routing accuracy, 94% instruction completeness
4. Stop iterating. File remaining gaps as future work.

**Result:** Loop terminated cleanly according to documented termination criteria.

## Anti-Patterns

- Never start a research run without a written baseline because without a before-score, you cannot distinguish genuine improvement from random variation, and every edit becomes guesswork rather than a measured intervention.
- Never skip the weaknesses analysis step between runs because without error analysis, you apply the same interventions every cycle, producing diminishing returns and eventually regressing skills that were already working.
- Never inflate scores by applying lenient criteria only to the current run and not the baseline because this produces a false convergence signal and masks whether the skill actually improved.
- Never rewrite the entire SKILL.md when targeted edits suffice because wholesale rewrites destroy validated content and frequently introduce regressions in areas that were already working correctly.

## Performance & Cost

### Model Selection

| Task | Recommended Model | Cost per run |
|---|---|---|
| Define quality signal | Sonnet | $0.05-$0.10 |
| Score skills (10 skills) | Sonnet | $0.10-$0.30 |
| Generate improvements | Sonnet | $0.10-$0.25 |
| Regression check (full suite) | Sonnet | $0.20-$0.50 |

### Token Budget

- **Scoring one skill:** ~500-800 tokens input, ~200 tokens output
- **Full regression suite (50 skills):** ~25K tokens input, ~5K tokens output
- **Expected context usage:** 3-6KB per iteration
- **Use context-optimizer** when running the autoresearch loop to manage large scoring outputs

## References

### Internal Dependencies
- `continuous-learning-v2` — Captures patterns discovered during autoresearch as instincts
- `skill-stocktake` — Identifies skills needing improvement before autoresearch
- `skill-generator` — Creates new skills from evolved patterns

### External Standards
- [Karpathy's Autoresearch Methodology](https://karpathy.github.io/2023/03/11/evals/) — Inspiration for eval-driven improvement loop

### Related Skills
- `continuous-learning-v2` — Follows autoresearch-loop to capture improvements
- `skill-stocktake` — Precedes autoresearch-loop to identify skill quality issues
- `skill-generator` — Creates new skills from evolved patterns

## Changelog

| Version | Date | Changes |
|---|---|---|
| 2.0.0 | 2026-07-09 | Upgraded to Gold Standard v2.0: added frontmatter version/category/dependencies, Identity with quality bar, Core Principles, Blocking Violations table, Verification with quality gates, References, Changelog. |
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